🧠 Introduction to Deep Learning

What is Deep Learning? Deep Learning is a subset of Machine Learning that uses artificial neural networks with multiple layers to learn from large amounts of data. It is designed to automatically discover features in unstructured data like images, audio, and text.

🤖 Machine Learning vs Deep Learning

Feature Machine Learning Deep Learning
Definition Uses algorithms to parse data, learn from it, and make decisions Uses multi-layered neural networks to learn from vast data
Data Requirements Works well with smaller datasets Requires large datasets
Feature Engineering Manual feature extraction needed Learns features automatically
Execution Time Faster to train Training is time-consuming
Explainability More interpretable Often considered a “black box”
Examples Decision Trees, SVM, k-NN CNN, RNN, Transformers

🚀 Real-World Applications of Deep Learning

🧠 Computer Vision
  • Face recognition: e.g., Face ID
  • Self-driving cars: Object detection and segmentation
  • Medical imaging: e.g., cancer detection
💬 Natural Language Processing
  • Language translation: e.g., Google Translate
  • Chatbots and virtual assistants: Alexa, Siri
  • Sentiment analysis
🔊 Speech and Audio
  • Speech recognition: e.g., voice typing
  • Voice cloning and synthesis: Text-to-Speech
🎯 Recommendation Systems
  • Netflix movie suggestions
  • Amazon product recommendations
💰 Finance and Fraud Detection
  • Credit card fraud detection
  • Stock price prediction

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